[1]} evaluate measures of domain similarity and their impact on dependency parsing accuracy. Given a target article to parse and a collection of annotated articles, they want to select the most similar articles to train the parser which is then evaluated on the target article. Both probabilistically motivated similarity functions—such as Jensen-Shannon divergence, and skew divergence—and geometrically motivated distance functions—such as cosine, euclidean, and variational distance functions—are evaluated on different features. [1]} find that comparing article topic distributions estimated by Latent Dirichlet Allocation (LDA) [3]} using variational distance function or Jensen-Shannon divergence can effectively find the most similar source, and using these automatic measures can outperform using human annotated genre labels. In addition to above mentioned similarity measures, [4]} explore to use Rényi entropy and Bhattacharyya coefficient to estimate the impact of domain similarity on cross-domain transfer.
